Lab 03 - Nobel laureates

Bale, Rolan, Alfred, Jack, Renee, Liz

{load-data , include=FALSE} knitr::opts_chunk$set(eval = TRUE)

© Nobel Prize Outreach. Photo: Hugh Fox © Nobel Prize Outreach. Photo: Hugh Fox

In January 2017, BuzzFeed News published an article on why Nobel laureates show immigration is so important for American science. You can read the article here. In the article, they show that while most living Nobel laureates in the sciences are based in the US, many of them were born in other countries. This is one reason why scientific leaders say that immigration is vital for progress. In this lab, we will work with the data from this article to recreate some of their visualizations as well as explore new questions.

Learning goals

Complete the following steps before you join the live workshop!

Workshop prep

You have two tasks you should complete before the workshop:

Merges and merge conflicts

You should by now have a bit of experience with Git and Github, and so now let us deliberately make a merge conflict so we can have more practice fixing them.

Git will put conflict markers in your code that look like:

<<<<<<< HEAD 

See also: [dplyr documentation](https://dplyr.tidyverse.org/)   

======= 

See also [ggplot2 documentation](https://ggplot2.tidyverse.org/)  

>>>>>>> some1alpha2numeric3string4

The ===s separate your changes (top) from their changes (bottom).

Note that on top you see the word HEAD, which indicates that these are your changes.

And at the bottom you see some1alpha2numeric3string4 (well, it probably looks more like 28e7b2ceb39972085a0860892062810fb812a08f).

This is the hash (a unique identifier) of the commit your collaborator made with the conflicting change.

Your job is to reconcile the changes: edit the file so that it incorporates the best of both versions and delete the <<<, ===, and >>> lines. Then you can stage and commit the result.

Complete the following steps during the live workshop with your team.

Merge conflict activity

Setup

Let’s cause a merge conflict!

Our goal is to see two different types of merges: first, we will see a type of merge where git cannot figure out what to do on its own (a merge conflict) and requires human intervention, then we will see another type of merge where git can figure out what to do without human intervention.

Doing this will require some tight choreography, so pay attention!

Take turns in completing the exercise, only one member at a time. Others should just watch, not doing anything on their own projects (this includes not even pulling changes!) until they are instructed to. If you feel like you will not be able to resist the urge to touch your computer when it is not your turn, we recommend putting your hands in your pockets or sitting on them!

Before starting: everyone should have the repo cloned and know which role number(s) they are.

Role 1:

🛑 Make sure the previous role has finished before moving on to the next step.

Role 2:

🛑 Make sure the previous role has finished before moving on to the next step.

Role 3:

🛑 Make sure the previous role has finished before moving on to the next step.

Role 4:

🛑 Make sure the previous role has finished before moving on to the next step.

Everyone: Pull, and observe the changes in your document.

Tips for collaborating via GitHub

Getting started

Packages

Before getting started with the Exercises, run the following code in the Console to load this package.

library(tidyverse)

Data: Nobel laureates

The dataset for this assignment can be found as a csv file in the data folder of your repository. You can read it in using the following.

nobel <- read_csv("data/nobel.csv")

The variable descriptions are as follows:

In a few cases the name of the city/country changed after the prize was given (e.g. in 1975 Bosnia and Herzegovina was called the Socialist Federative Republic of Yugoslavia). In these cases the variables below reflect a different name than their counterparts without the suffix _original.

Exercises

Take turns answering the exercises.

Get to know your data

  1. How many observations and how many variables are in the dataset? Use inline code to answer this question. What does each row represent?

There are some observations in this dataset that we will exclude from our analysis to match the BuzzFeed News results.

  1. Create a new data frame called nobel_living that filters for

Confirm that once you have filtered for these characteristics you are left with a data frame with 228 observations, once again using inline code.

🧶 ✅ ⬆️ Knit, commit, and push your changes to GitHub with an appropriate commit message. Make sure to commit and push all changed files so that your Git pane is cleared up afterwards.

Most living Nobel laureates were based in the US when they won their prizes

… says the BuzzFeed News article. Let us see if that is true.

First, we will create a new variable to identify whether the laureate was in the US when they won their prize. We will use the mutate() function for this. The following pipeline mutates the nobel_living data frame by adding a new variable called country_us. We use an if statement to create this variable. The first argument in the if_else() function we’re using to write this if statement is the condition we’re testing for. If country is equal to "USA", we set country_us to "USA". If not, we set the country_us to "Other".

Note that we can achieve the same result using the fct_other() function we have seen before (i.e. with country_us = fct_other(country, “USA”)). We decided to use the if_else() here to show you one example of an if statement in R.

nobel_living <- nobel_living %>%
  mutate(
    country_us = if_else(country == "USA", "USA", "Other")
  )

Next, we will limit our analysis to only the following categories: Physics, Medicine, Chemistry, and Economics.

nobel_living_science <- nobel_living %>%
  filter(category %in% c("Physics", "Medicine", "Chemistry", "Economics"))

For the next exercise work with the nobel_living_science data frame you created above. This means you will need to define this data frame in your R Markdown document, even though the next exercise does not explicitly ask you to do so.

  1. Create a faceted bar plot visualizing the relationship between the category of prize and whether the laureate was in the US when they won the Nobel Prize. Interpret your visualization, and say a few words about whether the BuzzFeed News headline is supported by the data.

    • Your visualization should be faceted by category.
    • For each facet you should have two bars, one for winners in the US and one for Other.
    • Flip the coordinates so the bars are horizontal, not vertical.

🧶 ✅ ⬆️ Knit, commit, and push your changes to GitHub with an appropriate commit message. Make sure to commit and push all changed files so that your Git pane is cleared up afterwards.

Aim to make it to this point during the workshop.

But of those US-based Nobel laureates, many were born in other countries

Hint: You should be able to cheat borrow from code you used earlier to create the country_us variable.

  1. Create a new variable called born_country_us that has the value "USA" if the laureate is born in the US and ”Otherotherwise. How many of the winners are born in the US?

  2. Add a second variable to your visualization from Exercise 3 based on whether the laureate was born in the US or not. Based on your visualization, do the data appear to support BuzzFeed News’s claim? Explain your reasoning in 1-2 sentences.

    • Your final visualization should contain a facet for each category.
    • Within each facet, there should be a bar for whether the laureate won the award in the US or not.
    • Each bar should have segments for whether the laureate was born in the US or not.

🧶 ✅ ⬆️ Knit, commit, and push your changes to GitHub with an appropriate commit message. Make sure to commit and push all changed files so that your Git pane is cleared up afterwards.

“Here’s where those immigrant Nobelists were born”

Note that your bar plot won’t exactly match the one from the BuzzFeed News article. This is likely because the data has been updated since the article was published. Do not worry if you cannot make the bar plot have all the bars in decreasing order of size—but you might like to try anyway!

  1. In a single pipeline, filter for laureates who won their prize in the US, but were born outside of the US, and then create a frequency table (with the count() function) for their birth country (born_country) and arrange the resulting data frame in descending order of number of observations for each country. Note that we are now no longer restricting to only the four science prizes. Which country is the most common? Make a (horizontal) bar plot of the data.

Hint: you will need to go back to the nobel_living data frame. You may wish to use mutate again to recreate the country_us and born_country_us variables.

🧶 ✅ ⬆️ Knit, commit, and push your changes to GitHub with an appropriate commit message. Make sure to commit and push all changed files so that your Git pane is cleared up afterwards.

Now go back through your write up to make sure you have answered all questions and all of your R chunks are properly labelled.

Finishing off

Once you decide as a team that you are done with this lab, all members of the team should pull the changes and knit the R Markdown document to confirm that they can reproduce the report.

Team member 1 should now consider removing the other team members as collaborators to prevent them from making any further changes to your GitHub repository.

Team members 2+ should now take a clone of the lab worksheet from team member 1 so that you have your own personal copy of today’s exercises.

Interested in how BuzzFeed News made their visualizations?

The plots in the BuzzFeed News article are called waffle plots. You can find the code used for making these plots in BuzzFeed News’s GitHub repo (yes, they have one!) here. You are not expected to recreate them as part of your assignment, but you are welcomed to do so for fun!